Overview

Dataset statistics

Number of variables15
Number of observations99003
Missing cells177
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.4 MiB
Average record size in memory173.8 B

Variable types

Numeric14
Categorical1

Warnings

age is highly correlated with dob_yearHigh correlation
dob_year is highly correlated with ageHigh correlation
friend_count is highly correlated with friendships_initiatedHigh correlation
friendships_initiated is highly correlated with friend_countHigh correlation
likes is highly correlated with mobile_likes and 1 other fieldsHigh correlation
likes_received is highly correlated with mobile_likes_received and 1 other fieldsHigh correlation
mobile_likes is highly correlated with likesHigh correlation
mobile_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
www_likes is highly correlated with likesHigh correlation
www_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
age is highly correlated with dob_yearHigh correlation
dob_year is highly correlated with ageHigh correlation
friend_count is highly correlated with friendships_initiated and 3 other fieldsHigh correlation
friendships_initiated is highly correlated with friend_count and 2 other fieldsHigh correlation
likes is highly correlated with likes_received and 4 other fieldsHigh correlation
likes_received is highly correlated with friend_count and 5 other fieldsHigh correlation
mobile_likes is highly correlated with likes and 3 other fieldsHigh correlation
mobile_likes_received is highly correlated with friend_count and 5 other fieldsHigh correlation
www_likes is highly correlated with likes and 1 other fieldsHigh correlation
www_likes_received is highly correlated with friend_count and 5 other fieldsHigh correlation
age is highly correlated with dob_yearHigh correlation
dob_year is highly correlated with ageHigh correlation
friend_count is highly correlated with friendships_initiatedHigh correlation
friendships_initiated is highly correlated with friend_countHigh correlation
likes is highly correlated with likes_received and 3 other fieldsHigh correlation
likes_received is highly correlated with likes and 3 other fieldsHigh correlation
mobile_likes is highly correlated with likes and 2 other fieldsHigh correlation
mobile_likes_received is highly correlated with likes and 3 other fieldsHigh correlation
www_likes_received is highly correlated with likes and 2 other fieldsHigh correlation
friendships_initiated is highly correlated with friend_countHigh correlation
dob_year is highly correlated with ageHigh correlation
age is highly correlated with dob_yearHigh correlation
likes_received is highly correlated with mobile_likes_received and 1 other fieldsHigh correlation
mobile_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
www_likes is highly correlated with likesHigh correlation
mobile_likes is highly correlated with likesHigh correlation
likes is highly correlated with www_likes and 1 other fieldsHigh correlation
friend_count is highly correlated with friendships_initiatedHigh correlation
www_likes_received is highly correlated with likes_received and 1 other fieldsHigh correlation
likes_received is highly skewed (γ1 = 112.0745682) Skewed
mobile_likes_received is highly skewed (γ1 = 107.5312999) Skewed
www_likes_received is highly skewed (γ1 = 126.257317) Skewed
userid has unique values Unique
friend_count has 1962 (2.0%) zeros Zeros
friendships_initiated has 2997 (3.0%) zeros Zeros
likes has 22308 (22.5%) zeros Zeros
likes_received has 24428 (24.7%) zeros Zeros
mobile_likes has 35056 (35.4%) zeros Zeros
mobile_likes_received has 30003 (30.3%) zeros Zeros
www_likes has 60999 (61.6%) zeros Zeros
www_likes_received has 36864 (37.2%) zeros Zeros

Reproduction

Analysis started2021-08-17 07:01:24.710419
Analysis finished2021-08-17 07:02:10.106997
Duration45.4 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

userid
Real number (ℝ≥0)

UNIQUE

Distinct99003
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1597045.208
Minimum1000008
Maximum2193542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:10.243601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000008
5-th percentile1060618.3
Q11298805.5
median1596148
Q31895744
95-th percentile2133357.1
Maximum2193542
Range1193534
Interquartile range (IQR)596938.5

Descriptive statistics

Standard deviation344059.1775
Coefficient of variation (CV)0.2154348391
Kurtosis-1.199556831
Mean1597045.208
Median Absolute Deviation (MAD)298438
Skewness0.0001076605667
Sum1.581122667 × 1011
Variance1.183767176 × 1011
MonotonicityNot monotonic
2021-08-17T12:32:10.427438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10485761
 
< 0.1%
10252251
 
< 0.1%
15730731
 
< 0.1%
14987371
 
< 0.1%
18509891
 
< 0.1%
14419351
 
< 0.1%
14273551
 
< 0.1%
14680061
 
< 0.1%
16052211
 
< 0.1%
21170851
 
< 0.1%
Other values (98993)98993
> 99.9%
ValueCountFrequency (%)
10000081
< 0.1%
10000131
< 0.1%
10000151
< 0.1%
10000381
< 0.1%
10000591
< 0.1%
10000611
< 0.1%
10000681
< 0.1%
10000941
< 0.1%
10001031
< 0.1%
10001251
< 0.1%
ValueCountFrequency (%)
21935421
< 0.1%
21935381
< 0.1%
21935221
< 0.1%
21934991
< 0.1%
21934851
< 0.1%
21934731
< 0.1%
21934681
< 0.1%
21934651
< 0.1%
21934601
< 0.1%
21934181
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.28022383
Minimum13
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:10.606196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q120
median28
Q350
95-th percentile90
Maximum113
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.58974831
Coefficient of variation (CV)0.6059445462
Kurtosis1.561446767
Mean37.28022383
Median Absolute Deviation (MAD)10
Skewness1.415260654
Sum3690854
Variance510.2967289
MonotonicityNot monotonic
2021-08-17T12:32:10.785454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185196
 
5.2%
234404
 
4.4%
194391
 
4.4%
203769
 
3.8%
213671
 
3.7%
253641
 
3.7%
173283
 
3.3%
163086
 
3.1%
223032
 
3.1%
242827
 
2.9%
Other values (91)61703
62.3%
ValueCountFrequency (%)
13484
 
0.5%
141925
 
1.9%
152618
2.6%
163086
3.1%
173283
3.3%
185196
5.2%
194391
4.4%
203769
3.8%
213671
3.7%
223032
3.1%
ValueCountFrequency (%)
113202
 
0.2%
11218
 
< 0.1%
11118
 
< 0.1%
11015
 
< 0.1%
1099
 
< 0.1%
1081661
1.7%
10798
 
0.1%
106125
 
0.1%
10580
 
0.1%
10473
 
0.1%

dob_day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53040817
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:10.943492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median14
Q322
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.015606359
Coefficient of variation (CV)0.6204647697
Kurtosis-1.188960111
Mean14.53040817
Median Absolute Deviation (MAD)8
Skewness0.1078407568
Sum1438554
Variance81.28115802
MonotonicityNot monotonic
2021-08-17T12:32:11.077378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17900
 
8.0%
104030
 
4.1%
153555
 
3.6%
53545
 
3.6%
123413
 
3.4%
23409
 
3.4%
33291
 
3.3%
173266
 
3.3%
203263
 
3.3%
143219
 
3.3%
Other values (21)60112
60.7%
ValueCountFrequency (%)
17900
8.0%
23409
3.4%
33291
3.3%
43217
3.2%
53545
3.6%
63108
 
3.1%
73010
 
3.0%
83202
3.2%
93003
 
3.0%
104030
4.1%
ValueCountFrequency (%)
311507
1.5%
302530
2.6%
292508
2.5%
282955
3.0%
272755
2.8%
262753
2.8%
253217
3.2%
242807
2.8%
232864
2.9%
222838
2.9%

dob_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1975.719776
Minimum1900
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:11.232537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1923
Q11963
median1985
Q31993
95-th percentile1998
Maximum2000
Range100
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.58974831
Coefficient of variation (CV)0.01143368032
Kurtosis1.561446767
Mean1975.719776
Median Absolute Deviation (MAD)10
Skewness-1.415260654
Sum195602185
Variance510.2967289
MonotonicityNot monotonic
2021-08-17T12:32:11.387464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19955196
 
5.2%
19904404
 
4.4%
19944391
 
4.4%
19933769
 
3.8%
19923671
 
3.7%
19883641
 
3.7%
19963283
 
3.3%
19973086
 
3.1%
19913032
 
3.1%
19892827
 
2.9%
Other values (91)61703
62.3%
ValueCountFrequency (%)
1900202
 
0.2%
190118
 
< 0.1%
190218
 
< 0.1%
190315
 
< 0.1%
19049
 
< 0.1%
19051661
1.7%
190698
 
0.1%
1907125
 
0.1%
190880
 
0.1%
190973
 
0.1%
ValueCountFrequency (%)
2000484
 
0.5%
19991925
 
1.9%
19982618
2.6%
19973086
3.1%
19963283
3.3%
19955196
5.2%
19944391
4.4%
19933769
3.8%
19923671
3.7%
19913032
3.1%

dob_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.283365151
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:11.519458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.529671569
Coefficient of variation (CV)0.5617485987
Kurtosis-1.240397572
Mean6.283365151
Median Absolute Deviation (MAD)3
Skewness0.03129550742
Sum622072
Variance12.45858138
MonotonicityNot monotonic
2021-08-17T12:32:11.629468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111772
11.9%
108476
8.6%
58271
8.4%
88266
8.3%
38110
8.2%
78021
8.1%
97939
8.0%
127894
8.0%
47810
7.9%
27632
7.7%
Other values (2)14812
15.0%
ValueCountFrequency (%)
111772
11.9%
27632
7.7%
38110
8.2%
47810
7.9%
58271
8.4%
67607
7.7%
78021
8.1%
88266
8.3%
97939
8.0%
108476
8.6%
ValueCountFrequency (%)
127894
8.0%
117205
7.3%
108476
8.6%
97939
8.0%
88266
8.3%
78021
8.1%
67607
7.7%
58271
8.4%
47810
7.9%
38110
8.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing175
Missing (%)0.2%
Memory size5.8 MiB
male
58574 
female
40254 

Length

Max length6
Median length4
Mean length4.814627434
Min length4

Characters and Unicode

Total characters475820
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male58574
59.2%
female40254
40.7%
(Missing)175
 
0.2%

Length

2021-08-17T12:32:11.913443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-17T12:32:12.025379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
male58574
59.3%
female40254
40.7%

Most occurring characters

ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter475820
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin475820
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII475820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e139082
29.2%
m98828
20.8%
a98828
20.8%
l98828
20.8%
f40254
 
8.5%

tenure
Real number (ℝ≥0)

Distinct2426
Distinct (%)2.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean537.8873749
Minimum0
Maximum3139
Zeros70
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:12.132537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q1226
median412
Q3675
95-th percentile1575
Maximum3139
Range3139
Interquartile range (IQR)449

Descriptive statistics

Standard deviation457.6498739
Coefficient of variation (CV)0.8508284359
Kurtosis2.199058275
Mean537.8873749
Median Absolute Deviation (MAD)213
Skewness1.535680925
Sum53251388
Variance209443.4071
MonotonicityNot monotonic
2021-08-17T12:32:12.280453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300173
 
0.2%
303170
 
0.2%
242164
 
0.2%
272163
 
0.2%
257161
 
0.2%
297161
 
0.2%
280160
 
0.2%
285160
 
0.2%
284158
 
0.2%
278158
 
0.2%
Other values (2416)97373
98.4%
ValueCountFrequency (%)
070
0.1%
160
0.1%
272
0.1%
379
0.1%
486
0.1%
592
0.1%
693
0.1%
784
0.1%
887
0.1%
993
0.1%
ValueCountFrequency (%)
31393
< 0.1%
31291
 
< 0.1%
31281
 
< 0.1%
31011
 
< 0.1%
30191
 
< 0.1%
29581
 
< 0.1%
29261
 
< 0.1%
28881
 
< 0.1%
28221
 
< 0.1%
27881
 
< 0.1%

friend_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2562
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.3507873
Minimum0
Maximum4923
Zeros1962
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:12.434487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q131
median82
Q3206
95-th percentile720
Maximum4923
Range4923
Interquartile range (IQR)175

Descriptive statistics

Standard deviation387.304229
Coefficient of variation (CV)1.972511719
Kurtosis50.09427289
Mean196.3507873
Median Absolute Deviation (MAD)64
Skewness6.059008484
Sum19439317
Variance150004.5658
MonotonicityNot monotonic
2021-08-17T12:32:12.596476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01962
 
2.0%
11816
 
1.8%
21117
 
1.1%
3860
 
0.9%
5789
 
0.8%
4749
 
0.8%
10737
 
0.7%
24732
 
0.7%
6720
 
0.7%
29719
 
0.7%
Other values (2552)88802
89.7%
ValueCountFrequency (%)
01962
2.0%
11816
1.8%
21117
1.1%
3860
0.9%
4749
 
0.8%
5789
0.8%
6720
 
0.7%
7671
 
0.7%
8718
 
0.7%
9700
 
0.7%
ValueCountFrequency (%)
49231
< 0.1%
49171
< 0.1%
48631
< 0.1%
48451
< 0.1%
48441
< 0.1%
48261
< 0.1%
48171
< 0.1%
48031
< 0.1%
47971
< 0.1%
47941
< 0.1%

friendships_initiated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1519
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.4524711
Minimum0
Maximum4144
Zeros2997
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:12.760454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median46
Q3117
95-th percentile418
Maximum4144
Range4144
Interquartile range (IQR)100

Descriptive statistics

Standard deviation188.786951
Coefficient of variation (CV)1.756934475
Kurtosis42.53560096
Mean107.4524711
Median Absolute Deviation (MAD)36
Skewness5.150757415
Sum10638117
Variance35640.51287
MonotonicityNot monotonic
2021-08-17T12:32:12.914333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02997
 
3.0%
12212
 
2.2%
21551
 
1.6%
31355
 
1.4%
41352
 
1.4%
61328
 
1.3%
51328
 
1.3%
111319
 
1.3%
81314
 
1.3%
131279
 
1.3%
Other values (1509)82968
83.8%
ValueCountFrequency (%)
02997
3.0%
12212
2.2%
21551
1.6%
31355
1.4%
41352
1.4%
51328
1.3%
61328
1.3%
71237
1.2%
81314
1.3%
91245
1.3%
ValueCountFrequency (%)
41441
< 0.1%
36541
< 0.1%
35941
< 0.1%
35381
< 0.1%
34151
< 0.1%
32381
< 0.1%
32331
< 0.1%
30861
< 0.1%
30781
< 0.1%
30241
< 0.1%

likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2924
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.0787855
Minimum0
Maximum25111
Zeros22308
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:13.068925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q381
95-th percentile726
Maximum25111
Range25111
Interquartile range (IQR)80

Descriptive statistics

Standard deviation572.2806808
Coefficient of variation (CV)3.666614134
Kurtosis200.4456878
Mean156.0787855
Median Absolute Deviation (MAD)11
Skewness11.02370356
Sum15452268
Variance327505.1777
MonotonicityNot monotonic
2021-08-17T12:32:13.219473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
022308
22.5%
16928
 
7.0%
24434
 
4.5%
33240
 
3.3%
42507
 
2.5%
52027
 
2.0%
61806
 
1.8%
71618
 
1.6%
81430
 
1.4%
91381
 
1.4%
Other values (2914)51324
51.8%
ValueCountFrequency (%)
022308
22.5%
16928
 
7.0%
24434
 
4.5%
33240
 
3.3%
42507
 
2.5%
52027
 
2.0%
61806
 
1.8%
71618
 
1.6%
81430
 
1.4%
91381
 
1.4%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
165831
< 0.1%
147991
< 0.1%
143551
< 0.1%
140501
< 0.1%
140391
< 0.1%
136921
< 0.1%
136221
< 0.1%

likes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2681
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.6893629
Minimum0
Maximum261197
Zeros24428
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:13.367478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q359
95-th percentile561
Maximum261197
Range261197
Interquartile range (IQR)58

Descriptive statistics

Standard deviation1387.919613
Coefficient of variation (CV)9.726861091
Kurtosis17384.94
Mean142.6893629
Median Absolute Deviation (MAD)8
Skewness112.0745682
Sum14126675
Variance1926320.851
MonotonicityNot monotonic
2021-08-17T12:32:13.524470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024428
24.7%
17305
 
7.4%
24541
 
4.6%
33347
 
3.4%
42669
 
2.7%
52373
 
2.4%
61873
 
1.9%
71680
 
1.7%
81538
 
1.6%
91351
 
1.4%
Other values (2671)47898
48.4%
ValueCountFrequency (%)
024428
24.7%
17305
 
7.4%
24541
 
4.6%
33347
 
3.4%
42669
 
2.7%
52373
 
2.4%
61873
 
1.9%
71680
 
1.7%
81538
 
1.6%
91351
 
1.4%
ValueCountFrequency (%)
2611971
< 0.1%
1781661
< 0.1%
1520141
< 0.1%
1060251
< 0.1%
826231
< 0.1%
535341
< 0.1%
529641
< 0.1%
456331
< 0.1%
424491
< 0.1%
395361
< 0.1%

mobile_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2396
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.1162995
Minimum0
Maximum25111
Zeros35056
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:13.678475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q346
95-th percentile481.9
Maximum25111
Range25111
Interquartile range (IQR)46

Descriptive statistics

Standard deviation445.2529851
Coefficient of variation (CV)4.195896268
Kurtosis360.9885806
Mean106.1162995
Median Absolute Deviation (MAD)4
Skewness14.16123656
Sum10505832
Variance198250.2207
MonotonicityNot monotonic
2021-08-17T12:32:13.837464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035056
35.4%
16297
 
6.4%
23941
 
4.0%
32917
 
2.9%
42265
 
2.3%
51794
 
1.8%
61598
 
1.6%
71395
 
1.4%
81212
 
1.2%
91149
 
1.2%
Other values (2386)41379
41.8%
ValueCountFrequency (%)
035056
35.4%
16297
 
6.4%
23941
 
4.0%
32917
 
2.9%
42265
 
2.3%
51794
 
1.8%
61598
 
1.6%
71395
 
1.4%
81212
 
1.2%
91149
 
1.2%
ValueCountFrequency (%)
251111
< 0.1%
216521
< 0.1%
167321
< 0.1%
140391
< 0.1%
135291
< 0.1%
129341
< 0.1%
126391
< 0.1%
121041
< 0.1%
120831
< 0.1%
119591
< 0.1%

mobile_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct2004
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.1204913
Minimum0
Maximum138561
Zeros30003
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:13.993468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333
95-th percentile317
Maximum138561
Range138561
Interquartile range (IQR)33

Descriptive statistics

Standard deviation839.8894437
Coefficient of variation (CV)9.984362083
Kurtosis15522.64932
Mean84.1204913
Median Absolute Deviation (MAD)4
Skewness107.5312999
Sum8328181
Variance705414.2777
MonotonicityNot monotonic
2021-08-17T12:32:14.142544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030003
30.3%
18243
 
8.3%
24948
 
5.0%
33608
 
3.6%
42944
 
3.0%
52383
 
2.4%
62022
 
2.0%
71745
 
1.8%
81521
 
1.5%
91437
 
1.5%
Other values (1994)40149
40.6%
ValueCountFrequency (%)
030003
30.3%
18243
 
8.3%
24948
 
5.0%
33608
 
3.6%
42944
 
3.0%
52383
 
2.4%
62022
 
2.0%
71745
 
1.8%
81521
 
1.5%
91437
 
1.5%
ValueCountFrequency (%)
1385611
< 0.1%
1312441
< 0.1%
899111
< 0.1%
733331
< 0.1%
434101
< 0.1%
307541
< 0.1%
303871
< 0.1%
273531
< 0.1%
207701
< 0.1%
189251
< 0.1%

www_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1726
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.96242538
Minimum0
Maximum14865
Zeros60999
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:14.304487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile208
Maximum14865
Range14865
Interquartile range (IQR)7

Descriptive statistics

Standard deviation285.5601519
Coefficient of variation (CV)5.715498191
Kurtosis449.1484832
Mean49.96242538
Median Absolute Deviation (MAD)0
Skewness16.91102529
Sum4946430
Variance81544.60033
MonotonicityNot monotonic
2021-08-17T12:32:14.452539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
060999
61.6%
14697
 
4.7%
22760
 
2.8%
31948
 
2.0%
41419
 
1.4%
51202
 
1.2%
61081
 
1.1%
7897
 
0.9%
8792
 
0.8%
9757
 
0.8%
Other values (1716)22451
 
22.7%
ValueCountFrequency (%)
060999
61.6%
14697
 
4.7%
22760
 
2.8%
31948
 
2.0%
41419
 
1.4%
51202
 
1.2%
61081
 
1.1%
7897
 
0.9%
8792
 
0.8%
9757
 
0.8%
ValueCountFrequency (%)
148651
< 0.1%
129031
< 0.1%
110771
< 0.1%
107631
< 0.1%
106271
< 0.1%
105391
< 0.1%
102551
< 0.1%
102321
< 0.1%
99021
< 0.1%
94311
< 0.1%

www_likes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1636
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.56883125
Minimum0
Maximum129953
Zeros36864
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size773.6 KiB
2021-08-17T12:32:14.610468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q320
95-th percentile227
Maximum129953
Range129953
Interquartile range (IQR)20

Descriptive statistics

Standard deviation601.416348
Coefficient of variation (CV)10.26853934
Kurtosis23812.2491
Mean58.56883125
Median Absolute Deviation (MAD)2
Skewness126.257317
Sum5798490
Variance361701.6237
MonotonicityNot monotonic
2021-08-17T12:32:14.777468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036864
37.2%
18513
 
8.6%
25111
 
5.2%
33586
 
3.6%
42828
 
2.9%
52317
 
2.3%
61918
 
1.9%
71602
 
1.6%
81445
 
1.5%
91373
 
1.4%
Other values (1626)33446
33.8%
ValueCountFrequency (%)
036864
37.2%
18513
 
8.6%
25111
 
5.2%
33586
 
3.6%
42828
 
2.9%
52317
 
2.3%
61918
 
1.9%
71602
 
1.6%
81445
 
1.5%
91373
 
1.4%
ValueCountFrequency (%)
1299531
< 0.1%
621031
< 0.1%
396051
< 0.1%
392131
< 0.1%
340391
< 0.1%
326921
< 0.1%
293371
< 0.1%
231471
< 0.1%
226441
< 0.1%
150961
< 0.1%

Interactions

2021-08-17T12:31:34.228206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:34.445303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:34.625820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:34.816312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:34.990920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:35.174512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:35.357470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:35.534487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:35.716416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:35.880494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:36.052586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:36.221366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:36.403523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:36.589470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:36.785373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:36.963562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:37.116596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:37.281531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:37.446509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:37.614590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:37.786265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:37.940360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:38.095974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:38.253551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:38.407140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:38.657506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:38.837503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:39.015826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:39.188697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:39.358407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:39.528204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:39.708959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:39.887843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:40.080583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:40.246344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:40.409591image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:40.569603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:40.740559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:40.918592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:41.098595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:41.281592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:41.455562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:41.639428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:41.817553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:41.982372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:42.145963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:42.314048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:42.479606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:42.648184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:42.816074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:42.975063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:43.145072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:43.317643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:43.489608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:43.657562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:43.835865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:44.140147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:44.324106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:44.489110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:44.667030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:44.842894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.013406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.179959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.348779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.511453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.680156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.836248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:45.991832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:46.170389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:46.350900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:46.532426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:46.716923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:46.882478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:47.056028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:47.223565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:47.393111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:47.570609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:47.757141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:47.929681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:48.097198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:48.263785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:48.438286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:48.614881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:48.798759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:48.966309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:49.134888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:49.290549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:49.450586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:49.615570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:49.785556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:49.953738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:50.114673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:50.263302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:50.422449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:50.583553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:50.904055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:51.069595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:51.243500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:51.409609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:51.573579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:51.727705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:51.904778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:52.067958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:52.231637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:52.397762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:52.553615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:52.704612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:52.865606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:53.031588image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:31:53.385400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:31:53.888880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:54.049845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:54.225391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:54.389951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:54.557562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:54.709308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:54.875543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:55.034605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:55.198660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:55.360878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:55.512698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:55.679728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:55.847647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:56.009377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:56.189184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:56.360620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:56.533201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:56.699785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:56.874318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:57.038848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:57.204436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:57.364373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:57.527280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:57.690692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:57.850756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:58.010301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:58.177390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:58.342646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:58.530587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:58.694575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:59.060578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:59.229863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:59.401685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:59.578761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:59.739374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:31:59.915687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:00.080800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:00.237724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:00.390645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:00.735604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:00.912906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:01.818582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:04.347472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:04.695555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:04.863478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-08-17T12:32:05.584482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:05.762550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:05.955767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:06.136480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:06.310439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:06.484389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:06.652545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:06.827482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:07.003476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:07.168542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:07.335597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:07.512983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:07.688863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:07.849909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:08.021450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-17T12:32:08.200972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-17T12:32:14.936473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-17T12:32:15.231461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-17T12:32:15.501452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-17T12:32:16.127435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-17T12:32:08.643816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-17T12:32:09.130869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-17T12:32:09.753242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-17T12:32:09.906811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

useridagedob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
020943821419199911male266.000000000
11192601142199911female6.000000000
220838841416199911male13.000000000
312031681425199912female93.000000000
41733186144199912male82.000000000
51524765141199912male15.000000000
61136133131420001male12.000000000
7168036113420001female0.000000000
8136517413120001male81.000000000
9171256713220002male171.000000000

Last rows

useridagedob_daydob_yeardob_monthgendertenurefriend_countfriendships_initiatedlikeslikes_receivedmobile_likesmobile_likes_receivedwww_likeswww_likes_received
989931654565191519948male394.04538414445011508844355961669127
98994206300620419931female402.01988332735110602572487333310332692
989951132164209199310female699.03611973450777684414690993859
989961668695242519894female182.0293812726018177655843117081756057
9899714589852814198512female290.022181618462610268429042503366018
98998126829968419454female541.021183413996180893505118874916202
989991256153181219953female21.01968172044011341243991059222820
990001195943151019985female111.0200215241195912554119591146201092
990011468023231119904female416.0256018545066516450657600756
990021397896391519745female397.020497689410124439410953002913